ABSTRACT Objective: To Evaluate the Understanding Study Habits of X Class Students in Krishna District of Andhra Pradesh. Method: A sample of 296 secondary school students of X class with variable such as boys verses girls, private and government, rural and urban and telugu and English medium in Krishna District, Andhra Pradesh were studied with 60 statements and four options were given for each statement and the analysis was performed keeping in view of the objectives framed, hypothesis formulated type of data collected, type of the tool used etc. using normative survey method by stratified random sample techniques and analysis of data was done include mean, mean percentage, standard deviation, C.R. etc. Results: School Education is meant for developing sound study habits to promote social efficiency. Due to several constants the research delimited the study to the comparative study on study habits of X class students. The research got that there is average knowledge level of the X class students towards the study habits including rural and urban X class students and the hypothesis was also rejected. Similarly in case of Government and Private schools also got rejected. There is no significant difference between boys and girls X class students on knowledge of study habits and the hypothesis was accepted for both Telugu and English Medium. Conclusion: School programmer should be more attractive so the students may attend in time and given proper guidance to go to the library for reading newspapers, magazines and other books of references. Students may be encouraged for participating in various socio-economic activities in their leisure time to become good citizens in future. Keywords: Habitat, Hypothesis, Survey, Environment.

Abstract: Predicting systemic financial crises and executing optimal asset reallocation during severe market stress remains a formidable challenge in algorithmic trading and quantitative risk management. Traditional classification models frequently fail in these environments due to high false-positive rates (the “boy who cried wolf” paradigm) and severe class imbalance, which often manifests as a rigid bias toward historically dominant safe havens like Gold. This paper proposes a personal-level, non-commercial Hybrid Machine Learning pipeline designed to resolve these structural flaws. The architecture consists of two stages: a Random Forest Classifier trained to detect Black Swan events using a refined set of 11 macroeconomic indicators, and a Multi-Output Random Forest Regressor that forecasts the 3-month expected returns of four primary asset classes (S&P 500, Gold, US Dollar, and 10-Year Treasury Bonds). By forecasting exact percentage returns rather than categorising “winning” assets, the framework natively circumvents safe-haven bias without relying on synthetic data generation techniques such as SMOTE. Stress testing against historical scenarios, including the 2020 pandemic crash and theoretical deflationary liquidity crunches, demonstrates that the model executes highly rational, dynamic reallocations. Validated through strict time-series cross-validation over a 26-year timeline, the framework effectively preserves capital while maintaining a near-zero false-positive detection threshold. Keywords: Black Swan Detection · Random Forest · Multi-Output Regression · Asset Allocation · Systemic Risk · Macroeconomic Indicators · Quantitative Finance · Walk-Forward Validation

Abstract— In the evolving digital landscape, cloud computing has become a cornerstone for scalable and flexible IT infrastructure. However, the rapid adoption of cloud services introduces a wide range of security threats, compliance challenges, and operational risks. This paper, From Threats to Trust: Securing Cloud Infrastructure through Resilience and Compliance presents a comprehensive analysis of cloud security challenges and explores effective mitigation strategies to enhance resilience and regulatory adherence. The study examines critical issues such as data breaches, insider threats, vendor lock-in, insecure interfaces, and distributed denial-of-service (DDoS) attacks within cloud environments. It further highlights the importance of robust security mechanisms, including identity and access management, encryption techniques, continuous monitoring, intrusion detection systems, and secure key management. Emphasis is also placed on compliance with global data protection regulations and the implementation of governance frameworks. By integrating best practices with proactive risk management approaches, this paper provides a structured framework for building secure, resilient, and compliant cloud infrastructures. The findings aim to support organizations in strengthening their security posture, ensuring data integrity and availability, and fostering trust in cloud-based systems. Keywords— Cloud Security, Cloud Computing, Identity and Access Management and Intrusion Detection Systems.

Abstract In today’s placement and recruitment environment, students receive a large number of emails containing important information related to hiring processes, assessment schedules, and job role requirements. Identifying relevant placement emails within this continuous stream remains a challenging and time-consuming task for many students. To address this problem, this paper presents an automated and explainable placement email classification framework built using core Natural Language Processing and Machine Learning techniques. The proposed system applies NLP-based text preprocessing and a Linear Support Vector Machine classifier to distinguish placement-related emails from general communications. The preprocessing pipeline includes text cleaning, tokenization, stopword removal, and lemmatization, followed by feature extraction using TF-IDF representations. This approach enables efficient handling of unstructured email content and improves classification performance. In addition to classification, a lightweight information extraction mechanism is incorporated to identify essential details such as company name, job role, and interview date from relevant emails. Experimental results demonstrate that the proposed model achieves high accuracy, precision, recall, and F1-score, indicating its effectiveness in filtering placement-related communications. The system ensures transparency and interpretability through the use of classical machine learning methods, making it suitable for real-world deployment in academic environments. Overall, the proposed framework helps students efficiently identify important placement emails and reduces the risk of missing critical opportunities. Keywords: Campus placement, natural language processing, support vector machine, email classification, TF-IDF, information extraction, explainable systems

Abstract As urban centers face escalating environmental pressures, the event industry's reliance on temporary, high-impact resource consumption is under scrutiny. This paper investigates the integration of Artificial Intelligence (AI) and Internet of Things (IoT) frameworks to optimize resource allocation in large-scale urban events. Moving beyond traditional manual management, we propose a "Smart-Green Model" that utilizes predictive analytics for waste reduction, energy efficiency, and logistics optimization. Based on a simulated pilot study of 250 event scenarios, results indicate a potential 30% reduction in carbon footprint. The study provides a strategic roadmap for municipal authorities and event planners to foster a technology-driven sustainable event ecosystem. Keywords: Artificial Intelligence, Sustainable Events, Resource Optimization, IoT, Urban Ecology.

Abstract This paper introduces an Al Career Navigator system. It uses a smart setup with several agents to automatically look at resumes, spot missing skills, find fitting job roles, and put together personalized learning plans. The system makes career decisions easier by offering clear, intelligent suggestions.

Abstract - This study examines customer satisfaction with products and services offered by Opulence Co-Working Pvt Ltd, focusing on service quality, infrastructure, pricing, and customer support. Primary data was collected through structured questionnaires. Statistical tools such as Correlation Analysis, Chi-Square Test, and T-Test were applied to analyze relationships, associations, and differences in satisfaction levels among respondents. The findings indicate that customers are generally satisfied with the services provided, though areas like responsiveness and affordability need improvement. The study concludes that enhancing service efficiency, maintaining reliable facilities, and strengthening customer support can improve satisfaction and customer retention in the coworking industry. Keywords: Customer Satisfaction, Service Quality, Co-Working Spaces, Correlation Analysis, Chi-Square Test, T-Test, Customer Support, Pricing, Infrastructure

Abstract Bank mergers have become increasingly common as financial institutions seek economies of scale, improved operational efficiency, and expanded market presence. However, mergers present significant regulatory compliance challenges due to differences in systems, processes, and regulatory reporting frameworks. Ensuring compliance with financial regulations such as anti-money laundering (AML), know-your-customer (KYC), and Basel III requirements becomes critical during post-merger integration. This study examines the role of information technology (IT) solutions in ensuring regulatory compliance during bank mergers. Using a case study approach of a mid-sized banking merger scenario, the research analyzes how integrated IT platforms—including core banking systems, regulatory reporting tools, AML monitoring systems, and data governance frameworks—support regulatory compliance throughout the merger process. The findings indicate that strategic deployment of IT solutions significantly reduces compliance risks, enhances transparency, and improves regulatory reporting accuracy. **Keywords—**Bank mergers, regulatory compliance, banking IT systems, AML, KYC, core banking integration, financial regulation.

Abstract: The urban ride-hailing industry now generates massive transactional datasets that, when subjected to rigorous analytical scrutiny, reveal actionable intelligence across pricing, fleet management, and demand planning. This paper presents a multi- dimensional business intelligence study conducted on a ride-hailing transaction corpus of 148,767 booking records spanning six vehicle categories — Auto, Bike, Go Mini, Go Sedan, Premier Sedan, and Uber XL — processed and visualised through an interactive four-page Microsoft Power BI dashboard. The analysis examines booking completion and cancellation dynamics (92,551 completed rides, 56,817 loss bookings, overall cancellation rate of 38.2%), disaggregated revenue contribution across vehicle tiers (total booking value INR 51,846,183), monthly demand seasonality across a full calendar year, customer payment instrument preferences across five channels, and structured cancellation attribution by party and reason. The dashboard architecture employs a persistent vehicle-type slicer, DAX-driven dynamic measures including the core Booking_value = SUM(uber[Booking Value]) formula, and four thematically distinct report pages delivering segment-level drill-down without requiring machine learning infrastructure. Key findings establish that Auto and Bike collectively account for 46.94% of total platform revenue, that UPI leads all payment channels, that February records the sharpest demand trough of the year, and that the predominance of blank cancellation reason records (approximately 55% of loss bookings) represents a critical data collection gap warranting structural remediation. The study demonstrates that well-structured Power BI analytics pipelines can surface decision-relevant operational insight from ride-hailing transaction logs in a form immediately consumable by fleet managers, operations analysts, and strategic stakeholders. Keywords — Ride-Hailing Analytics, Power BI, Business Intelligence, DAX, Booking Cancellation Analysis, Fleet Management, Revenue Distribution, Urban Mobility, Payment Method Analysis, Demand Seasonality, Data Visualization.

Abstract Air quality testing is essential for assessing environmental pollution and its impact on human health and ecosystems. Rapid industrialization, urbanization, and vehicular emissions have significantly degraded air quality worldwide. This research paper focuses on the principles, methods, instruments, and modern technologies used in air quality testing. It also highlights challenges and future advancements in monitoring systems. The study emphasizes the importance of accurate air quality assessment for sustainable development. Keywords Air Quality, Pollution Monitoring, AQI, Sensors, IoT, Particulate Matter, Environmental Engineering